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1,255 results for “High-resolution”
FIG. 3 in Description of a New Blind and Rare Species of Xyliphius (Siluriformes: Aspredinidae) from the Amazon Basin Using High-Resolution Computed Tomography
FIG. 3. Ventral view of head. (A) Xyliphius sofiae, ANSP 182322, holotype, 44.1 mm SL. (B) X. melanopterus, FMNH 99495, 120.4 mm SL. (C) X. lepturus, ANSP 128941, 94.5 mm SL. (D) X. barbatus, MLP 6798, 92.0 mm SL. Photos by M. Sabaj.
FIG. 8 in Description of a New Blind and Rare Species of Xyliphius (Siluriformes: Aspredinidae) from the Amazon Basin Using High-Resolution Computed Tomography
FIG. 8. HRXCT model of branchial arches (A–B, left side, dorsal view, anterior up), and 5th ceratobranchial of cleared and stained specimens (left side, dorsal view, anterior up). (A) Xyliphius sofiae, ANSP 182322, holotype, 44.1 mm SL (scale bar ¼ 2 mm). (B) Unobscured dorsal view of 5th ceratobranchial in ANSP 182322 (scale bar ¼ 1 mm). (C) Xyliphius lepturus, FMNH 99488, 72.1 mm SL (scale bar ¼ 1 mm). (D) Xyliphius melanopterus, FMNH 99493, 81.9 mm SL (scale bar ¼ 1 mm). bb: basibranchial; cb: ceratobranchial; cb5: ceratobranchial five; eb: epibranchial; hb: hypobranchial; pb: pharyngobranchial; tp: tooth patch.
FIG. 2 in Description of a New Blind and Rare Species of Xyliphius (Siluriformes: Aspredinidae) from the Amazon Basin Using High-Resolution Computed Tomography
FIG. 2. Lateral view of select species of Xyliphius. (A) X. barbatus, MLP 6798, holotype, 92.0 mm SL. (B) X. lepturus, ANSP 128941, 94.5 mm SL. (C) X. magdalenae, CZUT-IC 1288, 75 mm SL. (D) X. melanopterus, FMNH 99495, 120.4 mm SL. (E) X. kryptos, MCNG 27310, 112.0 mm SL. Photos by M. Sabaj (A), T. Carvalho (B, E), J. Garcia-Melo (C), and A. Thomaz (D).
FIG. 6 in Description of a New Blind and Rare Species of Xyliphius (Siluriformes: Aspredinidae) from the Amazon Basin Using High-Resolution Computed Tomography
FIG. 6. HRXCT model of select bones in Xyliphius sofiae, ANSP 182322, holotype, 44.1 mm SL. Bones associated with the anterior cephalic canals of the lateral line system (anterior is left) in dorsal (A) and lateral (B) views. Entire lateral ethmoid (anterior is left) in ventral (C) and frontal (D) view. (E) Partial lateral ethmoid in dorsal view cut to about half of its depth (anterior is left). (F) Partial lateral ethmoid in frontal view cut to about vertical through origin of lateral process. at: antorbital tubule; i1–i6: infraorbital branches one to six; io1: infraorbital one; iot: infraorbital tubules; lp: lateral process of lateral ethmoid; na: nasal; obc: olfactory bulb chamber; s1–s3: supraorbital branches one to three. Scale bar ¼ 2 mm.
FIG. 1 in Description of a New Blind and Rare Species of Xyliphius (Siluriformes: Aspredinidae) from the Amazon Basin Using High-Resolution Computed Tomography
FIG. 1. Holotype of Xyliphius sofiae, ANSP 182322, 44.1 mm SL, Río Amazonas in vicinity of Iquitos, Loreto, Peru. (A–C) Alcohol preserved (scale bar ¼ 5 mm). (D) Live. Photos by M. Sabaj.
FIG. 9 in Description of a New Blind and Rare Species of Xyliphius (Siluriformes: Aspredinidae) from the Amazon Basin Using High-Resolution Computed Tomography
FIG. 9. Ventral view of Weberian complex in select species of Xyliphius (anterior is top). (A) X. lepturus, FMNH 99488, 72.1 mm SL. (B) X. melanopterus, FMNH 99493, 81.9 mm SL. (C) Xyliphius sofiae, ANSP 182322, 44.1 mm SL. cv: complex vertebra; gbc: gas bladder chamber (line points to portion encapsulated by bone in B); hc: hemal canal; in: intercalarium; lal: lateral line tubules; pcv: parapophysis complex vertebra; pv5: parapophysis vertebra five; r6: rib 6; sc: scaphium; tr: tripus; v6: vertebra six. Scale bar ¼ 2 mm.
Methodological approaches to identifying and mapping fields of specific crops on a basis of high-resolution satellite images
<p>Supplementary materials v2 for the article Unagaev A, Korotkova I and Efremova N. "Methodological approaches to identifying and mapping fields of specific crops on a basis of high-resolution satellite images using phenological, geographic and regional statistical information"<br> </p>
High-Resolution P-Wave Tomography of the 1999 Izmit and Duzce Earthquake Rupture Zone
<p>hypo.gmtInitial-Initial hypocenter location displayed in the manuscript (Lat,Lon,Depth)</p> <p>hypo.gmtFinal-Final hypocenter location displayed in the manuscript Lat,Lon,Depth)</p> <p>VpPerc.out - Vp variation in percentange relative to the Initial 1D model (Lat,Lon, Depth, %Vp) </p> <p>VpAbsolute.out - Absolute Vp velocities (Lat,Lon, Depth, Vp) </p> <p> </p>
GDCLD:A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images
<p>GDCLD : A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images</p> <p>Fang, C., Fan, X., Wang, X., Nava, L., Zhong, H., Dong, X., Qi, J., and Catani, F.: A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-239, in review, 2024.</p> <p> </p> <p>Data description:</p> <p> </p> <p>The training dataset and the validation dataset are composed of UAV, PlanetScope, Gaofen-6 and Map World images of the 5 earthquake regions of Luding, Nippes, Hokkaido, Jiuzhaigou and Mainling. There is no overlapping area in each TIFF. The training dataset and the validation dataset are randomly divided at a ratio of approximately 0.75:0.25.</p> <p> </p> <p>train_dataset:</p> <p>train_data: The train dataset part of the GDCLD data set contains 11162 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 3) (TIFF).</p> <p>train_label: The train dataset part of the GDCLD data set contains 11162 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 1) (TIFF).</p> <p> </p> <p>Validation_dataset</p> <p>val_data: The validation dataset part of the GDCLD data set contains 4459 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 3) (TIFF).</p> <p>val_label: The validation dataset part of the GDCLD data set contains 4459 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 1) (TIFF).</p> <p> </p> <p>Test_dataset (Lushan, Sumatra, Mesetas and Palu dataset)</p> <p>This package contains the original files of remote sensing images from three sources: UAV, Map World, and PlaneScope belonging to the Lushan, Sumatra, Mesetas and Palu earthquake regiones, which are used to display the test area.<br><br>Future work:<br>The future work includes additional landslide data that the authors will continue to upload. In this 2.0 version update, we have added UAV imagery and interpreted data for loess landslides triggered by the December 2023 M6.2 earthquake in Gansu, China, with a resolution of 0.1 m. Due to authorization constraints, we can only provide PNG files without geographic coordinates. Additionally, this update includes PlanetScope imagery of landslides induced by heavy rainfall in Guangdong, China, in 2024, as well as PlanetScope imagery and landslide labels for events triggered by the Hualien earthquake in Taiwan.<br><br>Please note that this landslide dataset is publicly available exclusively for scientific research purposes and must not be used for commercial purposes.</p>
Stability of mitochondrial respiration medium used in high-resolution respirometry with living and permeabilized cells
<p>Excel and DatLab files</p>
Dataset for "Estimating high-resolution profiles of wind speeds from a global reanalysis dataset using TabNet"
<p>The dataset supports the article "Estimating high-resolution profiles of wind speeds from a global reanalysis dataset using TabNet", which is accepted to be published in the Environmental Data Science journal. <br><br>The description of the files is as follows:</p> <ol> <li>ERA5.nc: <br> <ul> <li>Dimensions: (location: 11, time: 166560)<br>Coordinates:<br> longitude (location) float32 ...<br> latitude (location) float32 ...<br> * time (time) datetime64[ns] 2000-01-01 ... 2018-12-31T23:00:00<br> year (time) int64 ...</li> <li>Variables: 10ws, 100ws, 100alpha, 975ws, 950ws, 975wsgrad, 950wsgrad, zust, i10fg, t2m, skt, stl1, d2m, msl, blh, cbh, ishf, ie, tcc, lcc, cape, cin, bld, t_975, t_950, 2mtempgrad, sktempgrad, dewtempsprd, 975tempgrad, 950tempgrad, sinHR, cosHR, sinJDAY, cosJDAY, 10ws_delta1, 10ws_delta2, 10ws_delta3, 10ws_delta4, 10ws_delta5, 10ws_delta6, 100ws_delta1, 100ws_delta2, 100ws_delta3, 100ws_delta4, 100ws_delta5, 100ws_delta6, 975ws_delta1, 975ws_delta2, 975ws_delta3, 975ws_delta4, 975ws_delta5, 975ws_delta6, 950ws_delta1, 950ws_delta2, 950ws_delta3, 950ws_delta4, 950ws_delta5, 950ws_delta6</li> </ul> </li> <li>2000.nc: <ul> <li>Dimensions: (obs: 11, time: 8784, heightAboveGround: 12)<br>Coordinates:<br> lat (obs) float64 ...<br> lon (obs) float64 ...<br> * time (time) datetime64[ns] 2000-01-01 ... 2000-12-31T23:00:00<br> * heightAboveGround (heightAboveGround) float64 10.0 15.0 ... 400.0 500.0<br>Dimensions without coordinates: obs<br>Data variables:<br> data (obs, time, heightAboveGround) float64 ...</li> </ul> </li> <li> 2001.nc: <ul> <li>Dimensions: (obs: 11, time: 8760, heightAboveGround: 12)<br>Coordinates:<br> lat (obs) float64 ...<br> lon (obs) float64 ...<br> * time (time) datetime64[ns] 2001-01-01 ... 2001-12-31T23:00:00<br> * heightAboveGround (heightAboveGround) float64 10.0 15.0 ... 400.0 500.0<br>Dimensions without coordinates: obs<br>Data variables:<br> data (obs, time, heightAboveGround) float64 ...</li> <li>Data is the wind speed at multiple height levels</li> </ul> </li> </ol>
A daily high-resolution surface net radiation dataset in China (2000-2019)
<p>Surface net radiation (Rn) characterizes the energy available at the Earth's surface and is essential for studying atmospheric, water, and carbon cycles. While some global-scale Rn products are available, they often suffer from issues such as data gaps, low resolution, and large uncertainties. Therefore, we developed a high-resolution (0.05°×0.05°) daily Rn dataset (named CHiRAD) from 2000 to 2019 in China using routine meteorological variables from more than 2400 stations and remotely sensed albedo. To ensure the reliability of the dataset, we tested a series of net shortwave and longwave algorithms using ground-based measurements and then employed the optimal combination of algorithms to generate this dataset. The dataset was validated against Rn observations from 43 flux towers across China. However, one may expect to use Rn data beyond this period in practice. To address this requirement, we also used AVHRR albedo to force the RI-PE algorithm and generated a long-series (1982–2020) Rn dataset (named Ext_CHiRAD). The only difference between the Ext_CHiRAD and CHiRAD, except for the length of the data series, is the source of albedo data. This dataset may not be as accurate as CHiRAD, but it has the advantage of a longer time span (1982–2020). This advantage makes it an ideal source of Rn data for hydrologic and environmental models to simulate long-term changes in target variables.</p>
High-resolution Global Dataset of BaP Based on Downscaling (0.1° × 0.1°)
<p>This dataset contains the annual- and monthly-averaged BaP concentrations in the atmosphere based on downscaling by using Relative emission with a resolution of 0.1° × 0.1°.</p>
High-resolution lithospheric shear velocity structure of the Suqian segment of the Tanlu fault zone from ambient noise tomography
<p>This file is the phase velocity dispersion curve manually picked up of Rayleigh wave in Suqian segment of the Tanlu Fault Zone. Only these Rayleigh wave dispersion curve are used for tomographic inversion.</p> <p>Rayleigh Wave Phase Dispersion Data (TL_SQ_Dispersion.zip):<br>Format:<br> Lon (station A) Lat (station A)<br> Lon (station B) Lat (station B)<br> Period (s) Vs (km/s)</p>
High-resolution lithospheric shear velocity structure of the Suqian segment of the Tanlu fault zone from ambient noise tomography
<p>Cross-correlation Functions Data(TL_SQ_CCFs.zip):<br>Format:<br> Lon (station A) Lat (station A) Elevation (A)<br> Lon (station B) Lat (station B) Elevation (B)<br> Time (t=0) GAB(t) GBA(t)<br> Time (t=dt) GAB(t) GBA(t) <br> Time (t=2dt) GAB(t) GBA(t)</p>
Data from: Redescription of Phymolepis cuifengshanensis (Antiarcha: Yunnanolepididae) using high-resolution computed tomography and new insights into anatomical details of the endocranium in antiarchs
Background. Yunnanolepidoids constitute either the basal-most consecutive segments or the most primitive clade of antiarchs, a highly diversified jawed vertebrate group from the Silurian and Early Devonian periods. Although the general morphology of yunnanolepidoids is well established, their endocranial features remain largely unclear, thus hindering our further understanding of antiarch evolution, and early gnathostome evolution. Phymolepis cuifengshanensis, a yunnanolepidoid from the Early Devonian of southwestern China, is re-described in detail to reveal the information on endocranial anatomy and additional morphological data of head and trunk shields. Methods. We scanned the material of P. cuifengshanensis using high-resolution computed tomography and generated virtual restorations to show the internal morphology of its dermal shield. The dorsal aspect of endocranium in P. cuifengshanensis was therefore inferred. The phylogenetic analysis of antiarchs was conducted based on a revised and expanded dataset that incorporates ten new cranial characters. Results. The lateroventral fossa of trunk shield and Chang's apparatus are three-dimensionally restored in P. cuifengshanensis. The canal that is positioned just anterior to the internal cavity of Chang's apparatus, probably corresponds to the rostrocaudal canal of euantiarchs. The endocranial morphology of P. cuifengshanensis corroborates a general pattern for yunnanolepidoids with additional characters distinguishing them from sinolepids and euantiarchs, such as a developed cranio-spinal process, an elongated endolymphatic duct, and a long occipital portion. Discussion. In light of new data from Phymolepis and Yunnanolepis, we summarized the morphology on the visceral surface of head shield in antiarchs, and formulated additional ten characters for the phylogenetic analysis. These cranial characters exhibit a high degree of morphological disparity between major subgroups of antiarchs, and highlight the endocranial character evolution in antiarchs.
Data from: Ancient mitochondrial DNA provides high-resolution time scale of the peopling of the Americas
The exact timing, route, and process of the initial peopling of the Americas remains uncertain despite much research. Archaeological evidence indicates the presence of humans as far as southern Chile by 14.6 thousand years ago (ka), shortly after the Pleistocene ice sheets blocking access from eastern Beringia began to retreat. Genetic estimates of the timing and route of entry have been constrained by the lack of suitable calibration points and low genetic diversity of Native Americans. We sequenced 92 whole mitochondrial genomes from pre-Columbian South American skeletons dating from 8.6 to 0.5 ka, allowing a detailed, temporally calibrated reconstruction of the peopling of the Americas in a Bayesian coalescent analysis. The data suggest that a small population entered the Americas via a coastal route around 16.0 ka, following previous isolation in eastern Beringia for ~2.4 to 9 thousand years after separation from eastern Siberian populations. Following a rapid movement throughout the Americas, limited gene flow in South America resulted in a marked phylogeographic structure of populations, which persisted through time. All of the ancient mitochondrial lineages detected in this study were absent from modern data sets, suggesting a high extinction rate. To investigate this further, we applied a novel principal components multiple logistic regression test to Bayesian serial coalescent simulations. The analysis supported a scenario in which European colonization caused a substantial loss of pre-Columbian lineages.
High-resolution 4D STEM dataset of SrTiO3 along the [1 0 0] axis at high magnification
<p>This dataset can be used to test various analysis methods for high-resolution 4D STEM, including phase contrast methods such as ptychography. Scan and diffraction coordinates have been calibrated. The high scan magnification allows to identify individual atoms and easily distinguish them from reconstruction artifacts.</p> <p>Data was acquired at a probe-corrected FEI Titan 80-300 STEM operated at 300 kV. The microscope was equipped with a Medipix Merlin for EM detector operated at an acquisition rate for individual diffraction patterns of 1 kHz. The scan size was 128 x 128 scan points and the recorded diffraction patterns had a dimension of 256 x 256 pixel.</p> <p>The convergence angle of the incident probe was measured with a polycrystalline gold specimen. Employing parallel illumination first, the (111) gold diffraction ring was used to calibrate the diffraction space assuming a lattice constant of gold of 0.4083 nm. With the known wavelength the convergence semi-angle was determined to 22.1 mrad from a Ronchigram recorded in the same STEM setting as used in the actual experiment. The convergence semi-angle in pixel was determined from the size of the primary beam on the detector.</p> <p>The rotation and handedness of the detector coordinate system with respect to the scan axes was determined by minimizing the curl of the first moment vector field and making sure that the divergence of the field is negative at atom positions. Note that, in theory, the curl of purely electrostatic fields should vanish. The pixel size in the scan dimension of 12.7 pm was taken from the STEM control software during live processing and verified by comparison with the known lattice constant of SrTiO<sub>3</sub>. The residual scan distortion, that is, the translation of the diffraction pattern as a whole during scanning, was not compensated for since it turned out to be negligible at the atomic-resolution STEM magnifications used in this analysis.</p> <p>The sample thickness was approximately 25 nm, determined by comparing the PACBED with simulation.</p> <p><strong>Parameters</strong></p> <p>Scan pixel size: 12.7 pm</p> <p>Center y: 126 px</p> <p>Center x: 123 px</p> <p>Convergence semi-angle: 22.13 mrad, 15.5 px</p> <p>Thickness: approx. 25 nm</p> <p>Affine transformation of the direction of scan coordinates to detector coordinates using https://github.com/LiberTEM/LiberTEM/blob/master/src/libertem/corrections/coordinates.py:</p> <pre>transformation = rotate_deg(88) @ flip_y() det_sy, det_sx = ((scan_sy, scan_sx) @ transformation)</pre> <p>See the included notebook for an exemplary analysis. See https://arxiv.org/abs/2106.13457 for more details.</p>
High-resolution modelling of uplift landscapes can inform micro-siting of wind turbines for soaring raptors
<p>Collision risk of soaring birds is partly associated with updrafts to which they are attracted. To identify risk-enhancing landscape features, a micro-siting tool was developed to model orographic and thermal updraft velocities from high-resolution remote sensing data. The tool was applied to the island of Hitra, and validated using GPS-tracked white-tailed eagles (<i>Haliaeetus albicilla</i>). Resource selection functions predicted that eagles preferred ridges with high orographic uplift, especially at flight altitudes within the rotor-swept zone (40-110 m). Flight activity was negatively associated with the widely distributed areas with high thermal uplift at lower flight altitudes (<110 m). Both the existing wind-power plant and planned extension are placed at locations rendering maximum orographic updraft velocities around the minimum sink rate for white-tailed eagles (0.75 m/s) but slightly higher thermal updraft velocities. The tool can contribute to improved micro-siting of wind turbines to reduce environmental impacts, especially for soaring raptors.</p>
Figure 4 in High-resolution survey indicates high heterogeneity in copepod distribution in the hydrologically active Drake Passage
Figure 4. Distribution of total zooplankton biomass (mg/m3) in the layer 0–200 m. (A) Distribution of total biomass; circle area proportional to biomass value. Background and scale below: temperature (◦C) at a depth of 10 m; dotted line: boundary of the Polar Front. (B) Average zooplankton biomass. (C) Distribution of the total zooplankton biomass along frontal transect; dotted line: boundary of the Polar Front.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.